ThinkWithPrompt by SynotticThinkWithPrompt

Measure brand awareness and share of voice

A practical way to track brand awareness and share of voice with the data and budget you actually have.

Marketing & BrandFew-ShotStandardAnalyzePlanFill-in Template

The prompt

Help me measure brand awareness and share of voice. Brand and category: [WHO WE ARE and what category buyers think of] Competitors to compare against: [LIST] Why we need this: [e.g. "justify brand spend", "track a rebrand", "enter a new market"] Budget for measurement: [e.g. "$0", "can run a survey", "have a social listening tool"] Data I already have: [branded search volume, direct traffic, social mentions, surveys, self-reported attribution answers] Build a measurement plan with: 1. Leading proxies I can track now: branded search volume vs. competitors, direct and branded organic traffic, share of search, social mentions and sentiment, earned media mentions, self-reported "How did you hear about us?". 2. Survey-based awareness: unaided and aided awareness, consideration, and preference questions; who to sample and how many respondents for a usable read; how often to run it. 3. Share of voice definitions (paid, organic, social, earned) and how to calculate each with my tools. 4. Linking awareness to business outcomes: what changes in conversion rates, CAC, or pricing power to watch. 5. Pitfalls: seasonality in branded search, news spikes, survey wording bias, small samples. Return: - A dashboard spec: Metric | Definition | Source | Frequency | Target. - A 10-question brand tracking survey. - A quarterly reporting template. Don't invent benchmark figures; if suggesting typical ranges, label them as rough and to verify.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • WHO WE ARE and what category buyers think of
  • LIST
  • e.g. "$0", "can run a survey", "have a social listening tool"

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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